Hey everyone! Studying data/numbers is one of my favorite parts of sports, especially in MLB. Creating a machine learning data model has been a goal of mine for a long time, and now that it’s here I can’t wait to share it with everyone. Let’s work 🫡 - t carm

Jack Flaherty vs Athletics Best odds for o6.5k’s = Bet 365

MacKenzie Gore model numbers vs the Padres today 📊

If you played his strikeouts, they cashed. Model was on point ✅

Kevin Gausman Model Report 📊

Hunter Brown Report 📊

t carm TCDataModel 📊 🤝🤝

Pitching model resport for Mets/Braves tonight: Grant Holmes o5.5 K’s 📈 Grifting Canning under 4.5 K’s 📉 Griffin Canning under 15.5 outs 📉 The rest of the lines were determined too sharp by the books and not worth the price. All of these lines were very close as well.

Home Run Model for Mets/Braves Tier 1: Ozun, Alonso, Soto Tier 2: Lindor, Riley, Acuna Jr

Grant Holmes o5.5 ✅ Canning left with an injury so I can’t take credit for those cashing. Model was 3-0 on strikeout props today.

Dylan Cease vs the Reds Best play according to the model ⤵️ Over 6.5 K’s with ladder potential 🪜

Pitching Model went 5-0 today with strikeouts. This thing keeps getting sharper and sharper

Pitching model had Hendricks o3.5 📈 and Soroka u5.5 📉 - Hendricks mainly because of low pitch locations with sinking motions vs LHB - Soroka struggling to get swinging strikes vs this lineup with mainly a sinker vs righties & an inflated line (public perception vs LA) ✅✅